Systems and methods for monitoring medication effectiveness

ABSTRACT

System and method for determining real-world effectiveness of various prescribed medications. Here a variety of different types of patient pulse wave measurements (e.g., blood pressure, pulse oximeter, ECG) and other physiological measurements are obtained. This actual data is compared to calculated measurements that would be expected based on various patient baseline measurements in the absence of medication, schedule of medications, and impact of medications the various patient baseline measurements. If the actual data meets expectations, then the medication is likely acting as anticipated. Depending on which types of data do not meet expectations, problems with one or more previously described medications may be reported. Other types of patient physiological readings, such as temperature, motion, lung function, brain wave function (EEG) and the like may also be obtained, these additional types of readings can be used to extend the range of different types of drugs/medications that the system can successfully monitor.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation in part of U.S. patent applicationSer. No. 16/673,611, filed Nov. 4, 2019, now U.S. Pat. No. 11,612,352issued Mar. 28, 2023; application Ser. No. 16/673,611 was a continuationin part of U.S. patent application Ser. No. 15/954,250, filed Apr. 16,2018, now U.S. Pat. No. 10,463,299 issued Nov. 5, 2019; application Ser.No. 15/954,250 was a continuation in part of U.S. patent applicationSer. No. 15/060,514, filed Mar. 3, 2016, now U.S. Pat. No. 9,946,844issued Apr. 17, 2018; application Ser. No. 15/060,514 claimed thepriority benefit of U.S. provisional application 62/138,377,“COMPREHENSIVE BODY VITAL SIGN MONITORING SYSTEM WITH NECK AND EARMOUNTED DEVICE, filed Mar. 25, 2015; application Ser. No. 15/060,514 wasalso a continuation in part of U.S. patent application Ser. No.14/186,151 “SIMULTANEOUS MULTI-PARAMETER PHYSIOLOGICAL MONITORING DEVICEWITH LOCAL AND REMOTE ANALYTICAL CAPABILITY”, filed Feb. 21, 2014, nowU.S. Pat. No. 10,022,053 issued Jul. 17, 2018; application Ser. No.14/186,151 in turn claimed the priority benefit of U.S. provisionalapplication 61/767,839 “SIMULTANEOUS MULTI-PARAMETER PHYSIOLOGICALMONITORING DEVICE WITH DUAL LOCAL AND REMOTE ANALYTICAL CAPABILITY”,filed Feb. 22, 2013; application Ser. No. 16,673,611 was also acontinuation in part of U.S. patent application Ser. No. 16/036,551,filed Jul. 16, 2019, now U.S. Pat. No. 10,893,837, issued Jan. 19, 2021;the entire contents of all of these applications are incorporated hereinby reference in their entirety.

BACKGROUND OF THE INVENTION Field of the Invention

This invention is in the field of patient operated medical diagnosticdevices that can be used to determine if a patient is being effectivelytreated by health care provider medication prescriptions.

Description of the Related Art

Although patients are often prescribed multiple medications, theeffectiveness of such prescriptions is often suboptimal. Suchineffectiveness can be due to different causes, such as patientnon-adherence to the prescriptions (e.g., not taking the prescribeddrugs properly), or alternatively, because the patient's body is notreacting to the drug (or drug combination) as expected or as desired(e.g., adverse drug interactions, and the like). In this latersituation, the patient's body may have originally reacted as expected,but then, due to various other factors, may with time or diseaseprogression, or unexpected interaction with other drugs may no longer bereacting with the drug as the patient's body did originally. Patientnon-adherence to health care provider mediation recommendations is amajor medical problem. Center for Disease Control (CDC) materialssuggest that between 20-30% of medication prescriptions are neverfilled, and medication is not taken as prescribed in up to 50% of allcases.

For example, studies have shown that only about 51% of patients beingtreated for hypertension are adherent to their medication therapy on along term basis. In this context, “long term” should be viewed as beingabout six months, since other studies have shown that medicationadherence rates drop off after the first six months of treatment. Thisis a large-scale problem. At present over 133 million Americans have along term chronic condition requiring medication.

It has also been estimated that medication non-adherence can result inup to 125,000 excess deaths annually; also incurring economic costs (dueto higher subsequent patient expenses) estimated at $100 billion to $300billion dollars per year.

Thus, methods to monitor and encourage patent adherence to prescribedmedications are of high interest in the art. Patient adherence tohypertension medication is particularly critical.

Patients, in particular elderly patients, are often put on multipledifferent medications at the same time. For example, to controlhypertension, patients may be put on various combinations of diuretics,angiotensin converting enzyme (ACE) inhibitors, angiotensin receptorblockers (ARBs), beta-blockers, vasodilators, calcium channel blockers,aldosterone antagonists, renin inhibitors, alpha blockers, and the like.No one drug alone may be totally effective, but in combination, severaldrugs may produce the desired results.

Patients with other types of disorders, such as lung disease, chronicobstructive pulmonary disease, epilepsy, diabetes, and the like are ofcourse not immune to hypertension. Many of these patients, sometimes inaddition to anti-hypertension drugs, also take additional types of drugsfor these disorders. It is not uncommon for these other drugs to alsohave an impact on cardiovascular system function as well.

In order to improve patient medication adherence, the patient shouldideally receive frequent feedback that would promptly warn the patientwhenever the patient is not adhering to their prescribed medicationproperly, or when this medication has otherwise become less than fullyeffective.

A few such patient operated medical diagnostic tests are presently onthe market, such as home blood glucose tests, home blood pressure tests,home pulse oximeters, and even home ECG tests.

With the exception of home tests for blood glucose, there are presentlyfew home diagnostic tests that can warn a patient when he or she is outof compliance for a particular medication. Here prior art home bloodpressure tests illustrate the problem—if a patient's blood pressure isnon-ideal, is this because the patient skipped one of severalanti-hypertensive medications that the patient has been taking, or is itsimply because the patient is having a bad day? If the patient skipped adrug, which one was skipped?

Thus, further improvements in the art of using patient operated medicaldiagnostics to monitor patient adherence to medication would bedesirable.

Blood Pressure Monitoring Technology:

Although automated oscillometric cuff type methods of obtaining bloodpressure and pulse wave profiles are highly popular in the art, othernon-cuff methods are also known. These include photoplethysmography,ballistocardiography, phonocardiography, advanced electrocardiographytechniques, video analysis, and radar methods.

Photoplethysmography methods are taught by the work of Lazazzera et.al., Sensors (Basel). 2019 Jun. 4; 19(11):2557; Xing et. al., Sci Rep 9,8611 (2019); Brophy et. al. Sensors. 2021; 21(18):6311; and Haddad et.al., IEEE J Biomed Health Inform. 2022 May; 26(5):2096-2105.

Ballistocardiography methods are taught by the work of Carlson et. al.,Sensors. 2021; 21(1):156; He et. al., Annu Int Conf IEEE Eng Med BiolSoc. 2012; 2012:5030-3; Kim et. al., IEEE Trans. Biomed. Eng. 2018, 65,2384-2391; Su et. al., IEEE Trans. Biomed. Eng. 2019, 66, 740-748, andChen et. al., US20150018637A1 (2015).

Phonocardiography methods are taught by the work of Foo et. al, Physiol.Meas. 27, 685 (2006).

Advanced Electrocardiography methods are taught by the work of Chen et.al., Med. Biol. Eng. Comput., vol. 38, no. 5, pp. 569-574, September2000; and Poon et. al., Proc. 27th Annu. Int. Conf. IEEE Eng. Med. Biol.Soc., Shanghai, China, 2006, pp. 5877-5880.

Video analysis methods are taught by the work of Verkruysse et. al., OptExpress. (2008) 16:21434-45; Jeong et. al., J Med Syst. (2016) 40:77;and Jain et. al., Conference Proceedings: 2016 IEEE 18th InternationalWorkshop on Multimedia Signal Processing (MMSP). (2016) 1-5.

Radar based methods includes the work of Alizadeh, Remote Monitoring ofHuman Vital Signs Using mm-Wave FMCW Radar. IEEE Access. PP. 1-1.10.1109/ACCESS.2019.2912956; Wang et. al., Remote Monitoring of HumanVital Signs Based on 77-GHz mm-Wave FMCW Radar. Sensors. 2020;20(10):2999; and Omer et. al., (2018) Non-invasive Glucose Monitoring atmm-Wave Frequencies. Journal of Computational Vision and ImagingSystems. 4. 3. 10.15353.

BRIEF SUMMARY OF THE INVENTION

The invention is based, in part, on the insight that various types ofpatient operated instrumentation, such as blood pressure monitors, pulseoximeters, ECG readers and the like discard a huge amount of data in thecourse of obtaining their various different types of pulse wavemeasurements and other types of measurements. This invention is alsobased, in part, on the insight that with proper analysis, this massiveamount of blood pressure data, pulse oximeter data, ECG reading data,and other types of data could be usefully employed to help solve themajor problem of monitoring issues of patient medication adherence andmedication effectiveness. In some embodiments, the invention may be amethod, device or system for determining any of an efficacy of amedication regimen, and any of the effectiveness of, or a patient'sadherence to, a prescribed medication regimen. The invention relies on aplurality of different types of measured (actual) patient pulse wavemeasurements, such as some combination of oscillometric blood pressuredata, oscillometric pulse oximeter data, and ECG data, as well as othertypes of patient physiological measurements as available.

In this context, medication effectiveness represents the total impact ofthe various medications that the patient is actually taking on thepatient's physiology (e.g., medical status), as compared to what theprescribing physician(s) (or other healthcare workers) may have intendedbased on the patient's medication regimen. The actual medicationeffectiveness may differ from what the prescribing physician intendeddue to multiple factors, including lack of patient medication adherence(e.g., the patient just is not taking the pills properly), adverse druginteractions, changes in patient medical status, unexpected sideeffects, and the like. Here the term “determining an effectiveness” isintended to communicate that the invention is configured to report or“sound an alarm” when any of these problems are detected. This alertsthe patient and healthcare to the fact that there is an unexpectedmediation problem, and that further investigation as to the underlyingcause of the problem may be needed.

As an analogy, consider a smoke detector versus a combined carbonmonoxide and smoke detector. “Determining patient adherence” is somewhatlike a fire detector, while “determining an effectiveness” is somewhatlike a combined smoke detector and carbon monoxide detector. Althoughlack of “effectiveness” is a more general problem than lack of“adherence”, and may require more follow up investigation to determineif the lack of effectiveness was caused by lack of adherence, adversedrug interactions, changes in patient medical status, or unexpected sideeffects, nonetheless determining (medication) effectiveness cansometimes be even more valuable than determining patient adherencebecause it can find more problems.

The invention further relies on additional information, such as patientreference (baseline) information that reports on the various patientpulse wave measurements in the absence of various types of medication,medication schedule information (which informs the invention as to whattypes of drugs/medications that patient should be taking, and when), andmedication impact parameters, which informs the invention as to how thevarious individual medications would be expected to impact (alter)various specific types of patient pulse wave measurements. Additionalinformation, such as blood glucose sensor data from blood glucosemeters, patient motion data from handheld computerized devices such assmartphones, skin electrical conductance sensors (e.g., galvanic skindetectors, also called GSR), sound analysis, and patient interview datacan also be used by the invention.

The invention will typically use at least one processor to obtainvarious different types of actual patient pulse wave measurements andoptionally other types of data as well. It will then use its varioustypes of additional information to determine if the actual data is asexpected based on the patient baseline pulse wave information, expectedmedication schedule, and expected impact of these medications on thepatient baseline pulse wave information. If the results areinconsistent, then the invention will typically conclude that thepatient is not responding as expected and that either the patient is notproperly adhering to his medication schedule, or that the medication isnot acting as expected/desired, and will report these problemsaccordingly.

Other types of patient physiological readings, such as temperature,motion, lung function (e.g. stethoscope-like microphone pickups andautomated sound analysis, spirometers, actual or computed respirationrate data), brain wave function (EEG), imported blood glucose data fromblood glucose sensors, motion data from accelerometers or motion sensorson handheld portable computerized devices such as smartphones, and thelike may also be obtained, and these additional types of readings can beused to extend the range of different types of drugs/medications thatthe system can successfully monitor.

Note that in some embodiments, “effectiveness” may be defined as animpact of at least one specific medication on a patient's actual patientpulse wave measurements as compared to calculated expected patient pulsewave measurements for this medication regimen.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a simplified drawing of patient reference pulse wavemeasurements for a normal (healthy patient) in the absence ofmedication. Three different types of patient pulse wave measurements(oscillometric blood pressure measurements, pulse oximeter typemeasurements, and ECG measurements) are being shown simultaneously,along with some of the underlying patient physiological mechanisms thatcreate some of these various patient pulse waves. Here the time elapsedfrom the last previous ECG “R” pulse (in milliseconds) is shown on the“X” (horizontal) axis. The “Y” vertical axis shows (for the bloodpressure measurements) the blood pressure in millimeters of mercury (mmHg), or arbitrary units for the other pulse wave measurements.

FIG. 2 shows a simplified version the reference pulse wave measurementsfor a different (older patient suffering from hypertension) patient inthe absence of medication. This is this patient's “baseline” pulse waveinformation. Note the overall higher blood pressure, and differenttiming of various components of the various pulse waves, relative toFIG. 1 .

FIG. 3 shows a how a specific type medication (here type “A” medication)can impact the pulse wave measurements for the hypertension patient fromFIG. 2 (above). The changes in the various and shapes of the curves canbe considered to be the “impact parameter” for this type of medication.The impact parameters can be expressed either analytically in terms ofthe impact of the drug on the underlying patient physiology, and/orempirically in terms of the changes in the shapes of the curves (withoutneeding to understand the mechanism by which the medication impacts thepatient's physiology). Here drug “A” lowers the patient's blood pressureoverall without otherwise causing much of a change in the timing of thevarious components of the various pulse waves.

FIG. 4 shows how a different specific type of medication (here type “B”medication) impacts the pulse wave measurements for the hypertensionpatient from FIG. 2 above. Here drug “B” has altered the timing of theECG “R” pulse, and has also lowered the blood pressure overall.

FIG. 5 shows how yet another different specific type of medication (heretype “C” medication) impacts the pulse wave measurements for thehypertension patient from FIG. 2 above. Here drug “C” has done severalthings. It has somewhat altered the timing between the ECG “R” pulse,and the onset in the rise in blood pressure. This drug has also alteredthe timing of some of the various underlying pulse waves (here directwave and reflected waves) so that they don't superimpose (augment) witheach other in an unfavorable manner. This helps reduce the peak(systolic) blood pressure.

FIG. 6 shows the effect of all three medications (type “A” and type “B”and type “C”) on the pulse wave measurements for the hypertensionpatient from FIG. 2 above. In this case, the effect of all three drugsis additive, and the hypertension patient's blood pressure is broughtback to almost “normal” or acceptable values.

FIG. 7 shows a flow chart showing of some of the various steps that maybe carried out by the medication effectiveness/adherence device'sprocessor in order to determine if the various physiologicalmeasurements taken by the device's various sensors are showing that thepatient is likely following his or her assigned medication schedule, ornot.

FIG. 8 shows an example of one type of patient operable instrumentationthat, with upgrades as described herein, may be used according to thepresent invention.

FIG. 9 shows an example of a different type of patient operableinstrumentation that, with upgrades as described herein, may be usedaccording to the present invention. In this embodiment, the patientoperable instrumentation is intended to be worn by the patient over aperiod of time.

DETAILED DESCRIPTION OF THE INVENTION

Medication (drug) effectiveness is defined by the Merck Manual as “howwell a drug works in real-world use”. This definition of effectivenesstakes into account the fact that essentially all drugs have arisk/benefit ratio, and that drugs frequently have side-effects, orundesired interaction with other medications (other drugs), that mightlimit their use in the real-world.

Thus, while a drug might appear to work well in clinical trials whenstudied by itself, in the real-world, where drugs can have undesiredside effects, and undesired interactions with other drugs, theperformance a drug that might appear to work well in a clinical studywhere patients are highly monitored and are just taking one drug, mightperform quite differently in the real world. In the real world,particularly for elderly patients, the situation is much morecomplicated. Patients, such as elderly patients, typically areprescribed multiple drugs at the same time. In the real world, thesedrugs may interact with each other in unexpected ways. Further, as thepatient ages, the function of various organs, such as the kidneys (whichclear drugs from the patient's body), can deteriorate. Anothercomplication is that patients may occasionally forget to take one ormore of their medications, or skip one or more of their medications ifthat medication is particularly expensive, and/or if the patient comesto believe that a given medication causes undesirable side effects. Thenet result is that when patients use a plurality of (e.g., multiple)drugs) in real-world situations, many variables come into play, and moresophisticated methods to analyze these situations are desirable.

In addition to prescribed drugs, other substances, such as commonly usedfoods, drinks, and nutritional supplements that a patient may ingest mayalso impact the patient's physiology, resulting in unexpected resultsdepending on that patient's medical status and medication schedule.

For example, grapefruit or grapefruit juice interacts with the patient'sdrug-metabolizing enzymes (such as CYP3A4) in the patient's liver andcan cause various drugs to build up to toxic levels, or otherwiseinteract start to produce unexpected medication results. Theseunexpected results can show up as various patient physiological changes,such as abnormal heart rhythms, low blood pressure, and difficultybreathing, which can be detected by the various methods of pulse waveanalysis described herein.

Unexpected changes in a patient's alcohol consumption, as well asunexpected interactions with certain nutritional supplements, can alsoresult in unexpected changes to a patient's physiology that can bepotentially detected by the various described herein.

Chronopharmacology: In addition to the impact of changing patientphysiology as the patient's health status changes, and the impact ofvarious foods and nutritional supplements on the patient, otherreal-world effects include medication timing (e.g.,chronopharmacological effects). Human physiology varies according to thepatient's circadian rhythms (circadian clock). For example, a patient'sability to absorb drugs through the patient's gastric system will differaccording to time of day. A patient's liver may metabolize differentdrugs differently according to time of day. Further, a patient's kidneyfunction (the kidneys often excrete drugs from the patient's body) canalso differ according to time of day. As a result, the function ofcertain drugs, such as anti-asthma drugs (e.g., theophylline), whichimpacts breathing and associated pulse wave detectable parameters, canvary according to time of day. The impact of certain antihypertensive(drug pressure) medications, which can impact both patient bloodpressure and pulse rates, can also be significantly impacted by the timeof day that a patient takes the drugs.

Thus, as a result of these real-world effects, even a patient that mightlook completely compliant on paper (e.g. always takes their prescribeddrugs once a day according to a written prescription) may experiencesignificant changes in real-world medication results due to changes inthe time of day the patient takes the drugs, changes in the patient'sphysiology (even those merely due to a normal aging process, let alonethose caused by various diseases), and interactions with various foodsand nutritional supplements. In some embodiments, the invention canserve as an “early warning” system to alert the patient and/or themedical practitioner that, for whatever reason, the patient's medicationregimen is not working as well “in the real world” as expected.

In this disclosure, the term “real-world effectiveness” is defined asbeing the effectiveness of a drug, outside of well control trials, whenused by non-clinical trial patients, under non-clinical trialconditions, who are subject to the various effects (drug-druginteractions, drug-food interactions, drug-nutraceutical interactions,variations in patient physiology caused by any of aging, genetics, ordisease, variations in patient medication adherence, andchronopharmacology effects caused by the time of day, or variations inthe time of day, that a patient takes their various drugs) describedabove.

As one example of how the invention can operate, consider the problem ofhypertension. Hypertension is a very common and very serious diseasethat is frequently treated by multiple anti-hypertensive drugssimultaneously. Often these different types of anti-hypertensive drugs(medications) have different, and sometimes even well understood,mechanisms of action on the user's cardiovascular system.

In this discussion, we will first examine some of the various types ofcardiovascular system related pulse wave data that may be obtained bypatient operable instrumentation, such as the easy to use multiplesensor instrumentation discussed in more detail in U.S. patentapplication Ser. No. 14/186,151 and 62/138,377, and shown in FIGS. 8 and9 . In these examples, we will examine some hypothetical automatedoscillometric cuff type blood pressure pulse wave profiles, automatedoscillometric pulse oximeter type pulse wave profiles, and automatedelectrocardiogram (ECG) pulse wave profiles, as well as some of theunderlying physiological changes brought about by hypertension andvarious drugs on these pulse wave profiles. These examples are intendedto make the general principles behind the invention easier tounderstand, but are otherwise not intended to be limiting.

In this discussion, automated blood pressure sensors, such asoscillometric cuff type blood pressure sensors will be commonlyabbreviated as “oscillometric” or “OSC” sensors regardless of if thesesensors actually use cuffs or not. That is for simplicity, other typesof blood pressure sensors will also be given this “OSC” label as well.These other types of blood pressure sensors will be discussed in moredetail shortly.

The automated pulse oximeter type sensors will be commonly abbreviatedas “pulse oximeter” or “POX” type sensors, and automatedelectrocardiogram sensors will be commonly abbreviated as “ECG” sensors.See application Ser. No. 14/186,151 and 62/138,377 for furtherdiscussion. Note that although in some embodiments, these three devices(blood pressure sensors, automated pulse oximeter type sensors, andautomated electrocardiogram sensors) will all be part of the sameunitized device, such as the same patient operable instrumentation, inother embodiments, one or more of these devices may be separate, andinstead communicate with the patient operable instrumentation via awired or wireless channel. For example, a unitized device such as shownin FIG. 9 , but lacking an automated oscillometric cuff type bloodpressure sensor, might implement the invention by communicating with aseparate oscillometric cuff type blood pressure sensor by any of a wiredor wireless (e.g., Bluetooth) connection, or by using a non-cuff-basedblood pressure sensor. For example, a processor from a device such asFIG. 9 might send commands to, and receive data from, a separateautomated oscillometric cuff type blood pressure sensor or non-cuffblood pressure sensor via a Bluetooth or other type link.

FIGS. 1-6 are based on a simplified model of the cardiovascular system.These figures show both the actual measurements that may be obtained bythe various pulse wave sensors, as well as a few details of some of theunderlying physiological mechanisms that produce these actualmeasurements.

In these simplified examples, assume that the major components of thepatient's systolic blood pressure caused by a combination of thepatient's primary pulse pressure (caused by contraction of the patient'sleft ventricle), augmented by a reflected wave produced when the primarypulse pressure wave effectively “bounces” off of the patient's majorarteries. In younger or healthier patients, these arteries are moreelastic, and this tends to delay the timing of the return “bounce” or“reflected” pressure wave so that it does not significantly augment thepressure of the primary pulse pressure wave. However, in older or lesshealthy patients, the arteries are less elastic, and this tends to speedup the timing of the return “bounce” reflected pressure wave so that thereflected wave pressure and the primary wave pressure additivelysuperimpose and augment each other, thus producing a higher (andtypically unhealthy) diastolic pressure.

FIG. 1 shows a simplified drawing of patient reference pulse wavemeasurements for a normal (healthy patient) in the absence ofmedication. Here the instrumentation is providing three types of“actual” patient “baseline” pulse wave measurements. These types includepulse waves from an electrocardiograph (ECG) type sensor (110), pulsewaves obtained from an oscillometric pulse oximeter type sensor (POX)(120), and pulse waves obtained from a blood pressure monitor, such asan oscillometric cuff type blood pressure monitor type sensor or othertype blood pressure sensor (OSC) (130).

The actually measured oscillometric type blood pressure waveforms areshown by the solid “OSC-Meas” line (130). These actually measuredwaveforms are produced by the combination of a primary pulse pressurewave “OSC-Prime” shown in dashed lines (132), and a reflected pulsepressure wave “OSC-refl” shown by the dashed line (134). For these “OSC”waveforms, the vertical “Y” axis should be assumed to read inmillimeters of mercury (mm Hg).

The actually measured pulse oximeter blood pressure waveforms are shownby the solid “POX-Meas” line (120). These actually measured waveformsare also produced by the combination of a primary pressure wave“POX-Prime” shown in dotted lines (122) and a reflected pulse pressurewave “POX-refl” (124). Although these measurements could also beexpressed in mm Hg, for better readability, the Y axis pulse oximeterreadings are shown as being offset from the oscillometric blood pressurereadings. Note that the timing and the shape of the pulse oximeterwaveforms are not quite the same as the oscillometric type waveforms.This is because the pulse oximeter sensor will typically be located in adifferent part of the patient's body (e.g., ear lobe, fingertip) thanthe oscillometric blood pressure sensor (arm, wrist), and the pulse wavewill thus take differing amounts of time to reach the two sensors, withthe time being controlled by the pulse wave velocity (PWV). Here assumethat in this case, the patient's pulse oximeter is mounted on an earlobe, while the oscillometric blood pressure sensor is mounted on awrist, and the signal pulse wave reaches the ear lobe first.

In this simplified model, assume that the horizontal “X” axis representsthe time (in milliseconds) since the peak of the last ECG “R” wave,which occurs at the same frequency as the patient's pulse. Assume alsothat the pulse transit time “PTT” (140) is the time delay between thepeak of the ECG “R” wave (112) and the rise in the measured pulse (142).

Here also, assume that the baseline of the measured oscillometricwaveform (OSC-Meas) (136) represents the patient's diastolic bloodpressure, while the peak of the measured oscillometric waveform (138)represents the patient's systolic blood pressure. These same conventionsand numbering scheme are used throughout FIGS. 1-6 .

In FIG. 1 , this ideal normal patient has ideal cardiac parameters, suchas a pulse rate of 70 beats per minute, and a blood pressure of 115/75mm/Hg. When the patient's heart beats (here shown triggered by the ECG“R” wave 112), the contraction of the patient's ventricle produces botha primary wave (OSC-Prime 132) and (due to rebound from the patient'sarteries), a time-delayed reflected wave (OSC-refl 134) that, dependingon the timing of the reflected wave, can augment or not augment thesystolic blood pressure (138) caused by the primary wave.

In this example, assume that the patient has young and flexiblearteries. As a result, the reflected wave (134) bounces more slowly, andis thus sufficiently delayed so that the peak pressure of the reflectedwave (134) does not add to the peak pressure of the primary wave (132),thus helping to keep the peak systolic pressure (138) at the desiredlevel. Additionally, due to the patient's more flexible arteries, thespeed of the pulse transit time (140) (e.g., time between the peak ECG“R” wave (112) and the raising part of the pulse wave 142) also tend tobe somewhat longer, and the patient's diastolic blood pressure (136) isalso at a lower level.

In this example, the processed pulse oximeter readings (120), (122),(124) are generally similar to the oscillometric cuff type bloodpressure readings or other type of blood pressure sensor readings, butdue to differences in the location of the two types of sensing device,differ somewhat in timing and pulse shape. Thus to summarize, in FIG. 1, the patient has a normal pulse rate of 70 beats per minute (this canbe seen by the fact that the ECG “R” wave (112) has a peak at 857milliseconds), the patient has a systolic blood pressure reading (138)of 115 mm Hg, a diastolic blood pressure reading (136) of 70 mm Hg, thereflected wave (134) arrives 130 milliseconds after the primary wave(138), and the patent has a pulse transition time (PPT, 140) of 200milliseconds.

FIG. 2 shows a simplified version of the same type of patient referenceinformation, but in this case, obtained from an older patient sufferingfrom hypertension. In the absence of any drugs, this older hypertensionpatient has a somewhat higher pulse rate (75 beats per minute) (notethat the ECG “R” wave now is at 800 milliseconds), a higher diastolicblood pressure (136) of about 95 mm Hg because this patient hasgenerally stiffer arteries. Additionally, this patient's coronaryarteries are also stiffer, thus, unfortunately, producing a fasteracting reflected wave (134) arriving only 60 milliseconds after theprimary wave (138). Due to this faster arrival time, the peak of thereflected wave (134) acts to augment the systolic blood pressureproduced by the peak of the patient's primary wave (132), producing ahigh peak systolic blood pressure of 160 over 95 (138). Additionally,the pulse transit time (140) (e.g., time between the peak ECG “R” wave(112) and the raising part of the pulse wave (142)) tends to be somewhatshorter, and is here 150 milliseconds.

Drugs (medications) alter patient physiology. Sometimes the underlyingmechanisms are known, and sometimes the underlying mechanisms are notknown. When the underlying mechanisms are known (or at least wellcharacterized from an analytical perspective), then the effects of thedrugs can be analyzed by decomposing the various measured signals intothe underlying physiological mechanisms or known analyticalcoefficients. When the underlying mechanisms are less well-known, oranalytical methods less adequate, alternative and more empiricalmethods, such as curve fitting, can instead be used to analyze patientdata. Here, these hypertension examples are useful, because bothanalytical and empirical methods may be used.

FIG. 3 shows a simplified diagram showing, with respect to FIG. 2 , themedication impact parameter for older hypertension patent 2 for a “typeA” specific type of medicine. Assume in this example that thismedication lowers both the patient's systolic and diastolic pressure bya certain amount (here assume 10 mm Hg for both values), but otherwisedoes not alter other cardiovascular parameters. Thus FIG. 3 generallyresembles FIG. 2 , except that the systolic (138) and diastolic values(136) are both 10 mm Hg lower.

FIG. 4 shows a simplified diagram showing, with respect to FIG. 2 , themedication impact parameter for patent 2 for a type “B” specific type ofmedicine. Assume that this medication reduces the patient pulse ratefrom 75 beats per minute to 70 beats per minute, and the beneficialeffects of this medication also reduce the systolic and diastolic valuesby about 10 mm Hg each (down to 150/85) but otherwise do not impact theother cardiovascular parameters. Here, drug “B” has not only lowered thesystolic (138) and diastolic (136) blood pressure readings by about 10mm Hg each, but it has also changed the timing of the ECG “R” pulse(112) from 800 milliseconds to 857 milliseconds. The timing of the otherpulse waveforms has also been altered accordingly.

FIG. 5 shows a simplified diagram showing, with respect to FIG. 2 , themedication impact parameter for patent 2 for a type “C” specific type ofmedicine. Assume that this medication makes the patient's arteries moreflexible, and thus changes (delays) the timing for the reflected wave(s) so that the peak of the reflected wave (134, 124) does not augmentthe pressure effects of the peak of the primary wave (132, 122) in suchan unfavorable manner. This change in reflected wave timing (say from anunhealthy 60 milliseconds to a better 110 milliseconds, or a plus 50millisecond increase) decreases the magnitude of the systolic portion ofthe blood pressure (138) from 160 mm Hg to 145 mm Hg. Additionally,assume that this drug also makes the patient's other major arteries moreflexible, such that the more flexible arteries have a longer pulsetransit time (140) from 150 milliseconds to 190 milliseconds, for a netgain of 40 milliseconds (ms) over that patient's original baselinevalues (shown in FIG. 2 ). Thus, the time between the peak ECG “R” wave(112) and the raising part of the pulse wave (142) tends to be somewhatlonger as well.

FIG. 6 shows a simplified diagram showing, with respect to FIG. 2 , thecombined medication impact parameters for patient 2 when the patient istaking all three drugs at once (e.g., drug “A”, drug “B” and drug “C”).Here the three drugs all act synergistically to bring the patient'sblood pressure from a formerly unacceptable level of 160/95(systolic/diastolic) to an acceptable target level of about 126/75 mmHg.

Here, the fact that the different types of drugs have both differentunderlying mechanisms of actions (on an analytical level), as well asdifferent effects on the shapes of the patient's pulse wave data (on anempirical level) can be used by the invention to help assess if thepatient is, or is not, in compliance with his or her medication schedulefor these three types of drugs.

For example, we know, (and this information can be encoded into themachine's plurality of medication impact parameters) that drug “B”, iftaken properly (e.g., according to that patient's medication scheduleinformation) should both slow the pulse rate (see the change in thetiming of the ECG “R” wave), and also lower both systolic and diastolicblood pressure by a certain expected amount (e.g., 10 mm Hg for each).Thus, differences in pulse rate timing (e.g., the ECG “R” pulse 112) canbe used to infer the presence of drug “B” using either analytical orempirical methods.

We also know that drug “C” changes the timing of the reflected wave(s)(124, 134) relative to the primary waves (122, 132). This reduces themagnitude to which the peak of the reflected wave augments the peak ofthe primary wave during the systolic portion of the pulse. This drugalso slows down the pulse transit time (here the distance from the “R”wave to the rise of the pulse) to a more normal level, and this shows upin other waveform changes as well.

Thus, differences in the timing of the reflected waves relative to theprimary waves can be used to infer the presence or absence of drug “C”.

Here the analytical or empirical methods differ, however. Using ananalytical approach, the shape of the measured waveform(s) such asOCS-Meas (130) and/or POX-Meas (120) could be analyzed and decomposed(e.g. using Fourier analysis) into the underlying OSC-prime (132),OSC-refl (134) and/or POX-prime (122), POX-refl (124) waveforms, andinferences as to the drug effects on the timing of the primary andreflected waves can be drawn directly.

In the analytical method, then with respect to the drug impactparameters for drug “C”, the drug impact parameters could report that atthis dosage, drug “C” changes the timing differences between the primarywave (132) and the reflected wave (134) by a time value such as from 60milliseconds to 110 milliseconds, (e.g. +50 milliseconds). Thus thisparticular medication impact parameter, for an analytical approach,might be a simple number such as +50 milliseconds. It might also be adifferent number, such as an artery elasticity parameter, that might, inturn, be converted to a different time delay parameter. But in eitherevent, we are decomposing the observed or “actual” waveforms into someunderlying equations, and the medication impact parameters can report onthe effects that the various drugs each have on the coefficients of theunderlying equations.

By contrast, in an empirical approach, which can be more useful when theunderlying mechanisms are not so well known, the medication impactparameters might instead store an average pulse waveform shape for thispatient when on the drug. The empirical method might instead look at thepatient 2 reference waveform (e.g., FIG. 2 OSC-Meas 130) and the pulsewaveform shape assumed to occur when this patent is taking drug C (e.g.,FIG. 5 ) and determine by curve fitting (e.g., model fitting, differenttypes of statistical regression analysis) if the observed patient 2 datafit the drug C pulse waveform shapes above a certain significancethreshold). In essence, we attempt to see how much of the actual orobserved data fits a drug “C” like pulse wave profile, and how much itresembles a baseline profile without drug, and attempt to infer thepresence of drug “C” without otherwise decomposing the waveform profilesinto underlying equation coefficients.

In terms of analyzing combinations of drugs, note that drug “A” acts tofurther decrease both the systolic and diastolic blood pressure to agreater extent than would be expected by the combination of drugs “B and“C” alone. Thus, the system can also assume (or infer or deduce) thatany further unexpected drop in systolic and diastolic blood pressure,not already assigned to drugs “B” and “C”, can likely be attributed todrug “A”.

Here, more channels of information (e.g., different types of actualpatent pulse wave measurements, obtained from different sensors) arehighly useful. This is because combining pulse wave measurements from atleast two different types of sensors (e.g., a plurality of differenttypes of actual patient pulse wave measurements) reduces noise, helpsconfirm positive signals, and can provide more insight than can anysingle type of measurement by itself.

It should be evident that in the examples above, any two of the varioustypes of pulse waves will give better results than one type of pulsewave information. Thus here, combinations such as at least ECG andOscillometric measurements, ECG and pulse oximeter measurements, oroscillometric and pulse oximeter measurements may be used. Combinationsof three or more types of pulse wave data can be still more useful andaccurate.

Thus, in some embodiments, the invention may be a method, device, orsystem for determining any of the effectiveness of a medication regimenor a patient's adherence to a medication regimen. The invention willtypically comprise, or use, multi-sensor patient operableinstrumentation such as that previously described in U.S. patentapplication Ser. No. 14/186,151 and 62/138,377, the entire contents ofwhich are incorporated herein by reference. See FIGS. 9 and 10 for someexamples. Other types of sensors and data sources may also be used.

This patient operable instrumentation will typically comprise at leastone computer processor (microprocessor, microcontroller, etc.), memory(at least one of local or remote memory) and various different types ofphysiological monitoring devices, each configured to obtain a pluralityof different types of actual patient pulse wave measurements. Theinstrumentation will typically be designed so that it is at leastcapable of operation by an average patient in the absence of ahealthcare practitioner. Of course, in some cases, this instrumentationmay alternatively be operable by an average caregiver who will typicallynot need to be a licensed or professional healthcare practitioner. Theidea, in any event, is to make the invention's instrumentationconvenient and easy to use so that it is used very frequently.

In a preferred embodiment, this patient operable instrumentation and itsvarious physiological monitoring devices will be configured to providevarious different types of actual patent pulse wave measurements.Preferably at least two different types of pulse wave measurements willbe obtained. For example, these various types of patient pulse wavemeasurements can include oscillometric pulse oximeter data (e.g., pulsewaves that report on varying blood oxygen saturation levels),electrocardiograph (ECG) readings, and pulse waves that report on bloodpressure measurements (e.g., oscillometric measurements from a cuff ornon-cuff type device).

In a preferred embodiment, the patient operable instrumentation will notconsist entirely of independent stand-alone monitoring devices. Instead,at least some and preferably all of the monitoring devices will form aunitized system where all devices are managed by at least one commonprocessor, preferably at least one common processor local to the patientoperable instrumentation. Here again, see the patient operableinstrumentation shown in FIGS. 9 and 10 as specific examples. Thispatient operable instrumentation will typically be referred to as “thedevice” for these discussions.

In order to determine if a patient is adhering to a particularmedication, or if a particular mediation is having the expected effecton the patient's physiology, the device, system, or method will, inaddition to patient baseline physiological data (e.g. data in theabsence of drugs/medication) also need to know the patent's medicationschedule information (e.g. what drugs is the patient supposed to take,and when) and the expected impact of these drugs (or at least the drugswhere monitoring is desired) on the various patient physiologicalparameters. For example, if a patient's doctor prescribes medication A,B, and C every day, the patient medication schedule information isessentially the prescription to take mediations A, B, and C every day.Medication schedules vary by number of drugs and number of times perday, often differ between patients, and are customized for particularpatients.

Thus, the invention will typically be configured to store and retrieve avariety of different types of information. This information can includemedication schedule information that pertains to at least one medicationand medication dosing schedule for at least one given patient. For thepatient shown in FIGS. 2-6 , for example, this can be a list or simplerecord stored in computer memory showing that this patient takes drugs“A”, “B”, and “C” on a daily basis, usually around 9:00 pm. Thus, thedevice, for example, can store medication schedule informationpertaining to at least one medication and medication dosing schedule forthat patient.

TABLE 1 Medication schedule information example Drug Days Times A Daily9:00 pm B Daily 9:00 pm C Daily 9:00 pm

The invention will also typically store a plurality of medication impactparameters. Here each individual medication impact parameter providesinformation on how a specific medication alters a specific type of pulsewave measurements. Here various types of data can be stored and useddepending on if the system processor is going to be using either ananalytical approach, an empirical approach, or both approaches. Anexample of the medication impact parameters for drugs “A”, “B”, and “C”on a patent similar to that shown in FIGS. 2-6 is shown below:

TABLE 2 Medication impact parameters example Drug Δ ECG-R Δ PTT ΔReflected Δ systolic Δ diastolic Waveform example(s) A 0 0 0 −10 mm/Hg−10 mm/Hg FIG. 3 curves B +57 ms 0 0 −10 mm/Hg −10 mm/Hg FIG. 4 curves C0 +40 ms +50 ms computed computed FIG. 5 curves

In this case, an “individual medication parameter” corresponds to asingle row in the above list or record stored in memory, and theplurality of medication impact parameters corresponds to the above tableas a whole.

The invention will also store a plurality of patient referenceinformation, where each individual patient reference informationprovides information on a specific type of patient baseline pulse wavemeasurements in an absence of a specific type medication. For thepatient in FIG. 2 , the patient baseline information in the absence ofall medication can include, for example, all of the information shown ordiscussed in FIG. 2 .

According to the invention, at least one processor (preferably at leastone local device processor, but alternatively also may be a remoteprocessor) is configured so that when the patient operableinstrumentation is used on the patient, this at least one processor willanalyze the plurality of different types of actual patient pulse wavemeasurements. Here the processor will know what time this analysis isdone relative to the medication schedule information. This timeinformation can be used to adjust the analysis accordingly. Thus, theprocessor may be configured so that if the readings are taken justbefore the patient was scheduled to take the medication, then anyaberrant readings can be discounted. However, if the readings are takenat several hours after the medication was expected, and aberrantreadings are still obtained, then the aberrant results may be given acorrespondingly higher weighting and be reported accordingly.

The invention will then use at least one processor to determine which ofthe various actual patient pulse wave measurements are inconsistent withcalculated patient pulse wave measurements that the system wouldnormally expect for that patient. This calculation is based on themedication schedule information, the time (here time means time and thedate) the actual data was taken, the various patient referenceinformation, and the various medication impact parameters.

The invention can then do various things with the results, in apreferred embodiment varying depending on what is found. The inventionmay just store the findings in memory, or inform the patient, or informcaregivers, or inform relevant healthcare professionals, or keep humanreadable records. At a minimum, however, the invention will least storea record in the invention's memory (either local and/or remote) of atleast those medications where inconsistent findings were found (e.g.,evidence that the patient was not responding to the medication as wouldbe expected).

In a preferred embodiment, the invention's at least one processor may befurther configured to provide patient alarm information. This patientalarm information could report when various patient physiologicalparameters, such as when the patient's blood oxygen saturation, bloodpressure measurements, electrocardiograph readings, or other sensorreadings fall outside of previously established boundaries. Theinvention may also be configured so that the device uses this patientalarm information, as well as those medications where inconsistentfindings were obtained, to determine and report that the patient mayeither be out of compliance with taking these expected medications orthat the medications are not acting as anticipated.

Such alarms can, in principle, be produced when an inconsistent resultis obtained from only one type of measurement of a single patientphysiological parameter (e.g., just blood pressure, just pulse) and onlyone drug (e.g., only one anti-hypertensive mediation) is used. However,the true value of the invention is most apparent in more complexsituations, where the patient is taking a plurality of medications, anda plurality of patient physiological parameters are measured, and thesystem is thus solving for multiple unknowns (effectiveness of drug a,drug b, drug c . . . ) using multiple variables (not only variablesbetween one type of physiological parameters, but also variables betweendifferent types of physiological parameters).

Value of Combining Different Types of Pulse Wave Measurements:

In a preferred embodiment, the invention will use its at least oneprocessor to determine if inconsistent findings were obtained over morethan one different type of actual patient pulse wave measurements. Forexample, do the blood pressure and pulse oximeter findings agree? Areany of these results consistent with the ECG results? Here the inventioncan be configured so when at least two different types of actual patientpulse wave measurements both report inconsistent findings (i.e., bothreport a possible medication effectiveness or adherence problem, so bothare consistent with each other), the device can more definitivelydetermine and report that the patient may be out of compliance withtaking those particular medications.

Methods of Obtaining Medication Impact Parameters:

In general, any method of obtaining the medication impact parameters maybe used. Here, however, although the gold standard would almostcertainly be experimenting on the patient by selectively withholding allmedications, and then introducing one at a time, and monitoring results,this method will often not be practical. This is because both doctorsand patients may (quite properly) object to deliberately withholdingimportant medication. Thus, various substitute methods will often beneeded in order to obtain the medication impact parameters in a saferand more ethically responsible way.

Often methods that attempt to estimate the mediation impact parametersbased on the patient's data in the known presence of adequatemedication, as corrected by the typical known effects of a givenmedication may be used. Here the probable changes in the various patientwaveforms caused by the various drugs can be calculated, and thesevalues used for the medication impact parameters. This is not as good asperforming the unethical experiment, but better than nothing.

In some embodiments, these various medication impact parameters can beobtained by taking averages over a plurality of similar type patients inthe presence and absence of a given medication (here results fromclinical studies may be used), and the differences between the variouspulse waveforms in the presence and absence of a given medication can becalculated based on such clinical study data. Again, this is not as goodas performing unethical experiments on the actual patient beingmonitored, but better than nothing.

Similarly, the various types of patient reference information (e.g.,various pulse wave waveforms in the absence of drugs) may be obtained byaveraging over a plurality of similar type patients in the absence ofthe various medications. Here again, data from clinical studies may beused. Alternatively, various mathematical models may be used. Again, notas good as performing unethical experiments on the actual patient beingmonitored, but better than nothing.

As will be described later on in this disclosure, the invention may alsobe configured so as to take advantage of “natural experiments” when thepatient has forgotten to take one or more medications to ethicallyobtain more accurate patient reference information.

In some embodiments, the invention may be configured so that thedevice's at least one processor calculates the expected patient pulsewave measurements from the previously stored medication scheduleinformation (and the time), a plurality of patient referenceinformation, and a plurality of medication impact parameters. Here theinvention can use its least one processor to transform the plurality ofpatient reference information into the expected patient pulse wavemeasurements by various mathematical operations. For each individualmedication (as expected from the medication schedule information), theseoperations can include:

-   -   1: selecting corresponding individual medication impact        parameters from list or record of various medication impact        parameters, thereby retrieving the selected individual        medication impact parameters.    -   2: Applying (using either analytical methods based on underlying        equations that attempt to reproduce aspects of the patient's        physiology, or by empirical curve fitting methods), these        selected individual medication impact parameters to the various        patient reference information, thereby producing intermediate        transformed patient reference information. For example, if the        patient reference information is as shown in FIG. 2 , then        applying drug “C” to this patient reference information would        produce the results shown in FIG. 5 , and so on.    -   3: Repeating the above steps until all of the individual        medication (drugs) in the medication schedule information has        been processed, thereby producing the expected patient pulse        wave measurements. For example, going down through the list, and        applying drug “A”, drug “B”, and drug “C” to the patient        reference data from FIG. 2 , thus producing FIG. 6 . If the        actual patient data resembles FIG. 6 , and the timing is        appropriate, then the patient is probably taking all three        drugs. If the actual patient data does not match, the invention        can then alternatively try further by seeing if any partial        combination of drugs fits the observed data. The system can then        report on its findings (based on which combination of expected        drugs best fits the observed data, and which deficiency in the        combination of expected drugs best fits the observed data.)

Other Types of Medication and Patient Monitoring Sensors:

Although for simplicity, much of the discussion so far has focused onpulse wave measurements and anti-hypertension drugs, it should beevident that the concepts disclosed herein may be used for a broaderrange of patient physiological measurements and a broader range of drugsas well,

In some embodiments, the invention may be further configured withadditional types of sensors. These sensors could include one or moreadditional types of sensors such as body temperature sensors, patentmotion sensors (e.g., accelerometers), lung function sensors (e.g.,microphones, spirometers), ECG electroencephalographic (EEG) sensors,and the like. In these embodiments, the various categories of storedinformation would be extended to accommodate these additional types ofphysiological data, drug types, and medication impact data.

For certain types of sensors, such as motion sensors, and blood glucosemonitoring data, common devices, such as smartphones (e.g., smartphonemotion sensors) and blood glucose monitors are inexpensive, widelyavailable, and often carried by patients throughout the day. Thesesmartphones and blood glucose monitors are often configured to delivertheir data via various wired or wireless (e.g., Bluetooth, Wi-Fi)computer links, upon demand. In some embodiments, the invention may beconfigured to retrieve such motion sensing data, blood glucosemonitoring data, and the like, store them in the invention's database,and use this information to help supplement information taken from theinvention's own sensors.

Thus, for example, the patient reference information data would beextended to further comprise baseline body information pertaining tothese additional types of sensors. This can be, for example, at leastone of baseline body temperature information, baseline patient motiondata, baseline patient lung status data, baseline patient blood glucoselevels, or baseline patient EEG data.

In some embodiments, the patient reference information data may befurther extended to also comprise the patient's baseline (or “normal”)drug response information (here called baseline-drug-response)pertaining to how the patient's body normally responds to the variousdrugs, as determined by the various sensors. This can be, for example,at least one of baseline-drug-response body temperature information,baseline-drug-response patient motion data, baseline-drug-responsepatient lung status data, baseline-drug-response patient blood glucoselevels, or baseline-drug-response patient EEG data. Generalizing thepatient reference information to further include how the patient wouldbe expected to normally respond to drugs (at least in the absence ofadverse drug interactions, other changes in the patient's medicalstatus, and the like) helps the system better determine situations wherethe effectiveness of one or more drugs may be compromised.

Similarly, the individual medication impact parameters can be extendedto further provide information on how a specific medication alters atleast one of these additional types of sensor data, such as baselinebody temperature information, baseline patient motion data, baselinepatient lung function, patient motion, patient blood glucose levels, andpatient baseline EEG data.

In these embodiments, the invention's at least one processor can befurther configured to use this (suitably extended) medication scheduleinformation, the known time of data acquisition, the various patientreference information and at least some of the individual medicationimpact parameters to further determine if at least some of theseadditional (e.g. non-hypertension) medications are also producinginconsistent findings (e.g. provide evidence that the patient is alsonot adhering to this additional medication as well).

In some embodiments, the invention can be configured to determine any ofdrug effectiveness and/or patient adherence to a medication regimenusing these inconsistent findings. Note that “using” these inconsistentfindings does not imply “using only” these inconsistent findings.Further, the system may be configured to use its at least one processorto establish or refine this effectiveness based on the patient'smedication adherence. Alternatively, or additionally, the system may beconfigured to output multiple scenarios, such as patient not adhering tothe mediation regimen and/or the mediation is not acting effectively. Insuch scenarios, the system may be configured to use its at least oneprocessor, or a remote processor, to query the patient, the patient'scaregiver, or automated pill dispensing or monitoring devices, and usesuch outside medication adherence information to further refine systemestimates that the mediation may not be acting effectively. The systemmay also be further configured to query the patient or caregiver as tochanges in the timing of drug administration, changes in the patient'sdiet, changes in any patient nutritional supplements, or changes inpatient health status (e.g., recent illnesses), and use this additionalinformation to further determine if the present patient medicationregime is not acting effectively in the real world.

To better visualize these various steps, processes, and methods,consider FIG. 7 . FIG. 7 shows a flowchart of some of the various stepsthat may be carried out by the device's processor. Here the patient'svarious medication impact parameters (702), which report on how eachindividual medication taken by the patent alters a specific type ofpulse wave measurements, are stored in memory. Additionally, patientreference information (704), which provides information on an individualpatent's pulse wave measurements in the absence of either allmedications, (exemplified by FIG. 2 ), or in the absence of at leastsome medications, are also stored in memory. The patient's medicationschedule information (reporting on which medications the patient istaking, and when (e.g., the dosing schedule) is also stored in memory(706).

When the patient operable instrumentation and its plurality ofphysiological monitoring devices (here exemplified by patient pulse wavedata such as rapidly time varying blood oxygen saturation levels (e.g.using a pulse oximeter type sensor), blood pressure readings (e.g. usingan oscillometric type blood pressure sensor), and electrocardiographreadings (e.g. using one or more ECG electrodes) is taken, this data isalso stored in memory as well (708) as a series of different types ofpulse wave measurements.

Once the patient pulse wave measurements (data) have been obtained, theinvention will then use at least one processor to analyze this data. Aspreviously discussed, there are two general approaches that can be usedhere, and the invention may use either approach or both approaches.

In a first, more analytical approach, the invention's processor(s) willattempt to analyze the various patient pulse wave measurements accordingto one or more analytical models. Using cardiac pulse waves as anexample, examples of such analytical models can include, but are notlimited, to models such as Moens-Korteweg equation, the Bramwell-Hillequation, the Waterhammer equation, Windkessel theory, and the like. Theinvention's processors may attempt to obtain fundamental values such asthe time differences between cardiac forward waves and reflected waves,systolic and diastolic blood pressure, pulse transit times (PTT), pulsewave velocities (PWV) and the like. This is shown in (710).

In a second, more empirical approach, the invention's processor(s)attempt to analyze the various patient pulse wave measurements accordingto a best fit to underlying basis curves approach (712). Here, theinvention will draw upon various underlying pulse wave curves thatdescribe the patient's pulse waves according to the effect of eachdifferent type of drug on that patient's baseline (e.g., in the absenceof all drugs) profile. When this option is used, the underlying patientpulse waves may be stored in memory as well. Here, for example, thepatient baseline curves may be stored as either part of the patientreference information (704), while the empirical impact of various drugson that patient's various types of pulse waves may be stored along withthe medication impact parameters (702). Other data storage schemes canalso be used.

Although the invention may use either the model based (710) or theempirical approach (712) on a stand-alone basis, in a preferredembodiment, which may have the advantages of being more noise resistantin some cases, the invention may determine which approach fits theunderlying data best, and then combine the results from both approachesto produce a weighted average of the two. Here, the approach that fitsthe underlying data the best (best fit may be determined by aleast-squares approach or other best fit determination algorithm) may begiven a higher weight. This optional but preferred embodiment is shownas (714).

Using either approach, the invention will then determine which likelymediation mix best fits the available pulse wave data (716).

In step (718), the invention's processors will then compare the observedmedication mix information (716) with the medication mix expected fromthe patient's medication schedule information (706). The processor mayemploy various rules or rubrics to help implement this comparisonprocess. For example, the processor may use rules such as, “the patientis likely to take all medications at once, or skip all mediations” inthis analysis. The medication impact parameter information (706) mayfurther contain information regarding the half-lives of the variousdrugs. Thus, for example, if a patient stopped taking all medication,the effects of the drug with the shortest half-life would diminishfirst, and the effects of the drugs with longer half-lives woulddiminish later.

If in step (718), the pulse wave results are clearly inconsistent withthe medication schedule information (706), then the analysis can clearlydetermine already that there is a problem, and report it at step (720)(or at least store this in memory, and preferably also notify a personor machine).

In some embodiments, the invention may further double check itsapparently OK results, and/or or alternatively report other types ofpotential medication problems. In these embodiments, the invention mayoften monitor other types of physiological parameters as well. Forexample, the patient operable instrumentation may be further configuredto include other types of sensors, such as accelerometers (particularlyuseful for patient worn instrumentation), patient temperature sensors,electroencephalographic (EEG) reading sensors, breathing status sensors(stethoscope-like microphones and sound analysis algorithms, spirometersensors), and the like. This other data from the other types of sensorswill be stored in memory as (722).

In this “extended other data” embodiment, the patient referenceinformation (704) may further include information pertaining to this“other data”, such as information pertaining to the patient's typicalmotion (e.g., does the patient tend to shake or move in an atypicalmanner), typical body temperatures, typical EEG waveforms (e.g., examplebaseline waveform for a patient with epilepsy), breathing parameters(e.g. respiratory data pertaining to conditions such as asthma) and thelike. This can not only act as a double check to help insure that anycardiovascular drugs are being adequately monitored, but can also beused to extend the variety of different types of drugs and medicalconditions that can be successfully monitored by the invention.

Although in some embodiments, the invention can be configured todetermine respiration rate directly from respiration sensors, in otherembodiments, the invention can be configured to infer or calculaterespiration rates based on other measurements. For example, duringrespiration, there are typically characteristic changes to the pulse,ECG, blood pressure, pulse oximeter data (e.g., photoplethysmogram/PPGdata) and the like. The pulse tends to slow down, and blood pressuretends to also drop on a frequency that varies according to respirationrate. Thus, in some embodiments, the invention can use variouscomputational methods, such as Fourier analysis, to mathematicallydetermine breathing rates from any of the ECG, pulse oximeter,oscillometric data used for blood pressure determination, and/or othersources.

Here the medication impact parameters (702) and medication scheduleinformation will also typically be extended to report on the impact ofthese other types of medications on the patient, as well as report onthe schedule by which the patient takes such other types of drugs.

For example, consider a patient with epilepsy or Parkinson's disease.Here, improper epilepsy or Parkinson's medication may not show up in thepatient's pulse wave measurements, but may show up as either abnormalmotion data (e.g., patient trembling, patient abnormal motion) or asabnormal EEG readings or even abnormal temperature readings.

At the same time, patients with other ailments, such as epilepsy orParkinson's disease, are hardly immune from common high blood pressureand other cardiac difficulties. Thus, in some embodiments, it is usefulto have the same patient operable instrumentation monitor drugs directedtowards completely different types of disease states.

In some other types of disorders, in particular breathing or lungdisorders (e.g. (asthma, chronic obstructive pulmonary disease, otherchronic lung disorders), the underlying medical condition and thevarious medications used to treat the lung disorder, can interrelatewith the patient pulse wave measurements. Thus, in these embodiments ofthe invention, the invention will also simultaneously evaluate theseother factors as well.

Consider the case where the patient's other (non-pulse wave) data (722)is also inconsistent with the patient's typical reference information(704) for this other type of (non-pulse wave data). This consistency canbe checked at step (724). The relationship between findings obtainedfrom this other (non-pulse wave) data at step (724) and the pulse wavedata findings (718), and the medication schedule information (706) canhelp with the analysis of both types of data.

For example, given that on a statistical basis, a patient that hasforgotten one type of medication is also more likely to forget to takeanother type of medication; findings of abnormal pulse wave values tendto make other types of abnormal data more significant. Similarly,findings of abnormal non-pulse wave data tend to make findings ofabnormal pulse wave data more significant.

Other drugs may also be monitored according to the invention, including:

Other cardiac drugs, such as ivabradine (Corlanor®), which acts to blockthe mixed sodium and potassium membrane channel (HCN) channel in certaincardiac pacemaker cells (sinoatrial SA node tissue). This inhibits thecardiac I_(f) current, and lowers the heart rate. Specifically the drugslows the diastolic depolarization, and thus its effects can be seen bychanges in the ECG pulsewave signal, along with other effects such as alower pulse rate. It has certain side effects, such as very slow heartrate (bradycardia), hypertension (high blood pressure), and can alsocause atrial fibrillation, ventricular fibrillation, prolonged ECG QTintervals, dizziness and weakness which the patient may also report. Insome embodiments, the system may also issue user warnings not to takethe medication if their blood pressure is too low (e.g., less than90/50), or if their heart rate is too low (e.g., <60 bpm), or if the ECGdetects heart block or prolonged QT intervals.

Neprolysin inhibitors drugs such as sacubitril (sold as thesacubitril/valsartan combination drug Entresto®), work by inhibiting anenzyme (neprilysin) that normally degrades vasoactive (natriuretic)peptides, resulting in a higher level of these vasoactive peptides, andis thus useful for treating heart failure and related cardiovascularproblems. These drugs have certain effects, and side effects, andadverse drug interactions that can be detected and reported by theinvention, including hypotension (low blood pressure), hyperkalemia, andcough (which could be detected by either a patient survey, or bymonitoring patient cough using a microphone (either built-in, or locatedon another computerized device such as a smartphone) and suitable coughanalysis software, such as the software disclosed in U.S. Pat. No.9,526,458, the entire contents of which are incorporated herein byreference).

Diabetes drugs and complications, including:

SGL2 inhibitors (sodium-glucose co-transporter 2) inhibitor drugs, suchas canagliflozin, dapagliflozin, empagliflozin, ertugliflozin,ipragliflozin, and the like are used to improve glycemic control in type2 diabetes. The human kidney operates by first excreting nearly allsmall molecules (such as glucose), but then using specific transporterproteins to then re-absorb important small molecules. The SGL2inhibitors interfere with this reabsorption, causing excess glucose tobe excreted from the body. Unfortunately, these drugs can have variousserious side effects, such as kidney failure, hyperkalemia, hypotension,ketoacidosis, and the like.

Here, at least some of these side effects can manifest themselves bychanges in pulse wave data and other sensor information, such as lowerblood pressure, elevated heart rate, and altered ECG data. Additionally,data imported from other sensors, such as blood glucose sensor data, canalso be used by the system to help determine if the patient is eitherdeveloping an undesirable side effect to the SGL2 inhibitor, has adversedrug interaction issues, or is simply noncompliant with one or moredrugs.

GLP-1 analogs (glucagon-like peptide 1 receptor agonists) drugs, such asalbiglutide, dulaglutide, exenatide, liraglutide, lixisenatide, andsemaglutide are also used to help treat type 2 diabetes. These drugs actto enhance pancreatic glucose-dependent insulin secretion, which isoften deficient in type 2 diabetics. These drugs also have their ownside-effects including increased heart rate, lowered systolic anddiastolic blood pressure, as well as characteristic changes in ECGprofiles such as changes in the ratio between low and high frequencycomponents (LF/HF ratio). Here again, data imported from other sensors,such as blood glucose sensor data, can also be used by the system tohelp determine if the patient is either developing an undesirable sideeffect to the GLP-1 analogs, has adverse drug interaction issues, or issimply noncompliant with one or more drugs.

The invention can also be used to investigate drug related problems thatresult in symptoms of diabetic ketoacidosis (DKA), which can be a drugrelated complication of diabetes, results in changes in serumelectrolytes (e.g., hypokalemia) and other problems that can result incharacteristic ECG differences, such as abnormal repolarizationpatterns, prolonged QT intervals and the like. Additional problems areabnormally high glucose levels (here blood glucose data can be importedby the invention), nausea (which can be reported using a patientinterview interface), trouble breathing (which can be directly orindirectly monitored) and the like. Here again, the system can review aprofile of the various drugs that the patient should be taking, andattempt to determine which drugs or drug interactions may be causingthese symptoms. Sometimes the problem may be a simple deficiency ofinsulin, but other times the problem may be caused by a more complexdrug interaction issue, and here the invention can be particularlyuseful in this later situation.

Narcotic opiate antagonists for weight loss include drugs such asContrave® (a naltrexone bupropion mixture) which can be taken over aperiod of time to assist in weight loss. This type of drug can havevarious types of side effects and adverse drug interactions, some ofwhich manifest as ECG changes (QRS prolongation) and cardiac arrhythmia.The drug also has certain adverse drug interactions with other drugs,such as MAO inhibitors (blood pressure increase, heart palpitations,tremors), CYP2D6 inhibitor drugs (may cause irregular heartbeat), andother medications. Here again, the invention can be used to monitor ifthe combination of drugs that the patient is taking is effective, or ifthe patient is obtaining ineffective results (either due tonon-compliance, adverse drug interactions, unexpected physiologicalissues, and other causes).

Opioids, Opioid Antagonists, and Pain Management Methods

Opioids, which are commonly and legitimately used for medical painmanagement purposes, are also notoriously abused to the point where thishas become a major public health crisis in the US and elsewhere. It canno longer be assumed, for an example, that an average patient who may betaking a plurality of prescribed drug medications is not also taking anundocumented opioid drug as well, or a prescription opioid at too high adose. Thus, undocumented opioid use can create unanticipated adversedrug reactions. Such adverse reactions can show up as abnormal(prolonged) ECG QT intervals, ventricular arrhythmia, respiratorydepression, and other signals that can be detected by the invention.

In some embodiments, the invention's algorithms may be configured so asto attempt to fit data according to a model where the patient (user) is,in addition to the prescribed drugs, also taking one or more drugs ofabuse, such as various opioid drugs according to an undocumented drug ofabuse mediation schedule. Thus, the invention may be configured todetermine if a good fit between the patient's official medicationschedule, known medical conditions, and invention sensor data, can beobtained by also assuming such an undocumented drug of abuse schedule.Although this is clearly sensitive information, the invention may alsobe configured to warn the patient, or the health care practitioner, orothers when such a fit has been found. If nothing else, the system canat least warn the patient about the risks of taking drugs of abuse whensuch a fit occurs.

Additionally, even when the opioid drug is being used according to theofficial medication schedule, the system can of course also inform whenthe opioid is causing adverse drug reactions or adverse druginteractions, or otherwise interfering with the effectiveness of thepatient's medication regimen.

Opioid antagonists: Certain opioid antagonists, such as Narcan®(naloxone HCL) are often used to help reverse opioid overdoses. Here theinvention may also be used to determine when Narcan should be used, ifan effective dose of Narcan has been administered, and if additionalmedical support, such as CPR, rescue breathing, and the like areindicated. Additionally, the system can warn about side effects ofNarcan (e.g., sudden opioid withdrawal symptoms) and can warnaccordingly as well.

Patient Controlled Analgesia (PCA) Devices:

Patient controlled analgesia (PCA) devices are commonly used to assistin patient pain management. They generally comprise computerizedsyringes or IV pumps that are often set to deliver a lower volume ofpain medication (often an opioid drug) to a patient's IV line, but whichcan also be configured to allow the patient to increase the amount ofmedication (usually by pressing a button) to help the patient bettermanage periods where the pain is more intense. To prevent accidentaloverdose, the PCA computer is configured to limit how much painmedication the patient is allowed to self-administer. Examples of suchsystems include Boydman, U.S. Pat. No. 5,069,668, and Bollish U.S. Pat.No. 5,957,885, the entire contents of both of which are incorporatedherein by reference.

In some embodiments, the devices and methods of the present inventionmay be used as part of a PCA control system. Here, for example, theinvention may be used to continually monitor patient vital signs in thecontext of all medication that the patient is receiving, and in thecontext of the patient's overall medical condition. Input from theinvention as to possible adverse drug reactions can then be used toautomatically adjust any of either the PCA baseline settings, or limitson the amount of drug medication that the patient is allowed toself-administer. These methods and systems can be particularly useful insituations where the patient is in intense pain, and is pushing the safelimits of drug use.

The invention may also be used to monitor or detect other signatures foradverse drug interactions as well.

Some other signatures of adverse drug interactions include prolonged ECGQT intervals, as well as a trending low blood pressure (often caused byunfavorable drug interactions). Here again, the invention can use thesensor data, and knowledge of the patient's drug schedules, to detect analert such trends, hopefully before severe symptoms begin.

Due to measurement noise and general physiological variability, oftenthe invention's methods may not produce results are entirely clear cut.Instead, the invention will typically obtain compliance or effectivenessdata that is best expressed in a more probabilistic numeric score, inwhich the system may judge, for example, that the probability that apatient is in compliance or that a given drug is effective, such as drug“C”, at any time is a probability number, such as 70%.

In some embodiments, data obtained from the non-pulse wave “other” datamay be used to change the weighting or significance level that thesystem uses to interpret its findings and report results. A patient witha borderline pulse and blood pressure data, as obtained from the pulsewave data, may be more likely to be scored as non-compliant with themedication schedule information if the patient is also exhibitingbreathing problems, and vice versa. Thus, in some embodiments, it may beuseful to compare findings between the pulse wave and non-pulse wavewings of the analysis and use this comparison to alter (728) thesignificance threshold (726) by which the system reports problematicfindings. This option (728) may be most useful when the significance ofany abnormal findings is somewhat uncertain. If it is very clear thatabnormal patient physiological parameters are being detected by the“other data” wing (722, 724), then these clearly abnormal results may bereported immediately (730). Here, the medication schedule information(706) may be used to suggest which medications may be at issue here.

Reporting Findings, and Interfacing with Remote Network ConnectedDevices:

In some embodiments, the invention may further be configured with means(e.g., touch screens, buttons, voice interface, network interface) toreceive compliance information from the patient. Here the patient (orcaregiver) can also report on periods when it is known that the patientwas out of compliance with at least one known medication. This, inessence, constitutes an ethical “natural experiment” that can be used toobtain further calibration information for the invention. To takeadvantage of this ethical “natural experiment’, the invention's at leastone processor can be further configured to use this compliance (or lackof compliance) information to select at least some of the actual patientpulse wave measurements for use in establishing or refining at leastsome of the patient's medication impact parameters and/or the patientreference information.

Say that the patient has forgotten all medications for several days.Here the system can at least take advantage of this unfortunate fact andgather more accurate patient reference information.

In some embodiments, the invention may be further configured withnetwork interface means (e.g., WIFI or Bluetooth™ or wired or other typeof computer network interface) to allow the invention to connect with atleast one remote computerized device over a network (preferably acomputer network). Here the invention's at least one processor can befurther configured to report at least some of those medications whereinconsistent findings were obtained to a remote computerized device(e.g., a caretaker device, a patient smartphone tablet, or computerdevice, a healthcare professional computerized device, and so on).

In these embodiments, the invention's at least one processor can befurther configured to enable any of the various medication impactparameters, patient reference information, and medication scheduleinformation to be uploaded or downloaded over a network from a remotecomputerized device. Here, for example, a healthcare professional, uponprescribing a new medication, might also contact the invention andupload the medication, preferred use schedule, and medication impactinformation to the invention so that patient compliance and/or drugeffectiveness can then be monitored.

Note that more specifically, and as previously discussed, according tothe invention, effectiveness can also be viewed as comprising an impactof at least one specific medication on a patient's actual patient pulsewave measurements, as compared to calculated expected patient pulse wavemeasurements, for a medication regimen comprising a plurality of drugsthat the patient is expected to take.

In some embodiments, the invention may also be configured to, eitherdirectly, or by way of other computerized devices such as wirelesslyconnected smartphones, tablets, and the like, query the patientregarding changes in the patient's health status. Thus the patient can,in some embodiments, input useful information such as reporting nausea,sweating, trembling, changes in urination, and other symptoms that thesystem may incorporate into its various decision algorithms.

Examples of Patient Operable Instrumentation:

FIG. 8 shows an example of one type of patient operable instrumentationthat may be used according to the present invention. This type of deviceis discussed in more detail in copending application Ser. No. 14/186,151and its provisional application 61/767,839; the entire contents of bothof which are incorporated by reference in their entirety.

FIG. 9 shows an example of a different type of patient operableinstrumentation that may be used according to the present invention. Inthis embodiment, the patient operable instrumentation is intended to beworn by the patient over a period of time. This type of device isdiscussed in more detail in provisional patent application 62/138,377,the entire contents of which are incorporated herein by reference. Thisdevice (900) can comprise an optional ear attachment device (902) thatmay have any of a temperature sensor and a pulse oximeter sensor. Thedevice can also comprise a neck mounted device (904) that may contain anECG sensor, batteries, and a computer processor. This neck mounteddevice may also comprise other types of sensors such as accelerometers,lung function sensors (e.g. microphones and audio processing circuitryto provide stethoscope like lung function assessment), and the like.

In some embodiments, other types of devices may also be attached to theneck mounted device. These other devices may include arm connectors(906) and armature wires that extend down to patient worn ECG electrodes(908), and the like.

User Medication Adherence Methods, and Machine Learning Methods:

In some embodiments, the invention may be configured to receive inputfrom the user pertaining to which medications the user believes he orshe has taken. Here, the invention may query the user regarding thesemedications (usually via a graphical user interface such as a touchsensitive display, or other methods such as audio queries and machinelanguage recognition of audio responses).

For example, the invention may ask the user questions or surveys suchas: “Did you take your medication [here the system may also provide anoptional list of medications] on time today?” and give appropriatestatistical weight to the user's responses. Here, for example, a “no”answer or a “non-response” answer might make the system give higherweight to the possibility that the user (patient) is not properlyadhering to his or her medication schedule. Other survey questions caninclude questions regarding the patient's recent health status such asquestions pertaining to nausea, abdominal pain, feeling generally weak,shortness of breath, cough, recent colds or flu, and so on.

Similarly, the invention may also be configured to receive inputrelating to other indicia of user/patient cooperation with healthcareprofessional recommendations. For example, input pertaining if thepatient is adhering to other parts of the user/patient's health careplan, such as taking vital sign readings, adhering to scheduledmeetings/consultations, providing on-time answers to questionnaires, andthe like can also provide further information as to that user/patient'slikely adherence to their medication schedule, or medicationeffectiveness if the patient is, in fact, adhering to the medicationschedule.

Thus, for example, in situations where the physiological readings as tomedication adherence, such as in FIG. 7 steps (724 and 726) are unclear,evidence that the patient has indicia of not cooperating with otherhealthcare recommendations may be used to alter the significance levelsat which medication non-adherence is reported as being a potentialproblem. Conversely, if the patient has indicia of cooperating withhealthcare recommendations, then other problems, such as lack ofmedication effectiveness, possibly caused by unexpected physiology,adverse drug interactions, changes in the patient's underlying medicalcondition, etc. can be reported.

Additionally, user surveys or behavioral data with regards to otheraspects of healthcare compliance can also be used to extend the utilityof the invention for a broader range of medications. This user survey orbehavioral data may, for example, be particularly useful for patents(e.g., psychiatric patients) that may also be on medication that may notproduce obvious changes in pulse wave data, for example.

Additionally, in some embodiments, various types of machine learningmethods may also be used to enhance the utility of the invention.Examples of such machine learning methods include supervised learningapproaches, such as the k-nearest neighbors algorithm (KNN) (see N. S.Altman, “An Introduction to Kernel and Nearest-Neighbor NonparametricRegression”, The American Statistician 46(3), 1992, pages 175-185).Other machine learning methods, such as artificial neural networks(ANNs) methods (Ganesan et. al., “Application of Neural Networks inDiagnosing Cancer Disease Using Demographic Data”, International Journalof Computer Applications (0975-8887) Volume 1—No. 26, 2010); use ofsupport vector machines or support vector networks (SVMs), (Cortes andVapnick, “Support-vector networks”, Machine Learning September 1995,Volume 20, Issue 3, pp 273-297) and the like may also be used.

These machine learning models may be pre-trained using data frominvestigational studies. Alternatively, or additionally, the input forthese supervised machine learning techniques can be obtained fromvarious combinations of the patient or user input, including healthcareprofessional input, and/or from the patient caretaker input as well.

Other types of machine learning methods, including unsupervised andreinforcement type machine learning, may also be used.

In some embodiments, these machine learning models will also employfeatures that could include any combination of the inputs gatheredwithin the invention, such as the various physiological signals (orderived metrics), patient alarms, medication scheduling, patientdemographic information, and the like.

The machine learning models (or indeed the invention in general) may, inturn, provide various types of output pertaining to patient medicationadherence. In some embodiments, the output may be represented as abinary output (e.g., an automated “yes”/“no” assessment as to medicationadherence, or medication effectiveness).

In other embodiments, the learning models (or again, the invention ingeneral) may provide multi-value outputs pertaining to patientmedication adherence and effectiveness. For example, the patient may bescored on a non-binary scale (such as very compliant, moderatelycompliant, and non-compliant). In other embodiments, the learning models(or again, the invention in general) may provide numerical scores as tolikely patient compliance (e.g., a 0.0-10.0 score, such as 9.8 out of10.0). At least to the extent that the patient is compliant, then otherunexpected results can be attributed to lack of medicationeffectiveness. Other compliance and effectiveness output ratings, suchas graphical or calendar data that show likely periods of compliance andnon-compliance, or periods of medication effectiveness and the like mayalso be provided. In general, any graphical, alphanumeric, sound or eventactile output pertaining to patient medication compliance or medicationeffectiveness may be provided by the invention.

As previously discussed, non-cuff type methods of obtaining bloodpressure and blood pressure related pulse wave data includePhotoplethysmography (PPG), Ballistocardiography, Phonocardiography,Advanced-Electrocardiography, Video Analysis, and high resolution(GHz+frequency) Radar. Here these methods are discussed in more detailbelow.

Photoplethysmography (PPG)

The volumetric change in blood flow measured through optical sensors isreferred to as Photoplethysmography (PPG). PPG channels are oftenmeasured at 660 nm (red), 940 nm (infrared), and 525 nm (green)wavelengths. PPG sensors are either reflective (commonly placed on theforehead and wrist locations), or transmittance-based (commonly placedon the fingers, toes, ear lobes, and nasal cavity).

As PPG detects the changes in blood volume, it captures the pulsewaveform, and as such provides information about the pulse rate, pulserate variability, and the pulse morphology. The PPG waveform is commonlyused in conjunction with the ECG waveform in order to estimate the pulsetransit time (PTT), which can be used to accurately estimate bloodpressure. However, the PPG waveform can also be used to estimate bloodpressure without the use of other biosignals. This has been demonstratedusing both pulse wave velocity (PWV) and pulse wave analysis (PWA)techniques.

Some examples of PWV-based techniques include

-   -   Estimation BP based on PTT determined using multiple PPG sensors    -   Estimation BP based on PTT determined using PPG combined with        height and weight measurements

Some examples of PWA-based techniques include:

-   -   Estimation of BP using PPG-based machine learning methods    -   Estimation of BP using PPG-based multi-linear regression

Ballistocardiography

The Ballistocardiography (BCG) waveform measures vibrations that areproduced by the body during the cardiac cycle. The ejection of bloodduring a cardiac cycle causes a reaction of the body associated withsmall variations in displacement, velocity, and acceleration. As such,there are various sensors that can be employed to measure the BCG, butthese typically consist of some combination of an accelerometer(measurement of force/acceleration), magnetometer (measurement of amagnetic field), and/or gyroscope (measurement of angular rate). Theseform the basis of an inertial measurement unit. For example,piezoelectric films on chairs and beds, and force plates that measurerecoil on a standing scale. Compared to most other methods, BCGmeasurement can be performed with minimal contact (no skin contactrequired) and is suitable for very long-term use.

Analysis of the BCG can be used to determine heart rate and heart ratevariability, as well as other cardiovascular parameters. Furthermore,the BCG has been shown to have potential applications in cuffless bloodpressure estimation and blood pressure estimation in combination withthe PPG.

Phonocardiography (PCG)

The phonocardiography (PCG) waveform represents heart sounds associatedwith the opening and closing of the heart valves during the cardiaccycle. The PCG waveform can be acquired using a digital stethoscope inorder to acquire a high fidelity recording of the four main heart sounds(S1, S2, S3, S4) that occur during each heartbeat.

Analysis of the PCG can be used to determine heart rate and heart ratevariability. The PCG can also be used for detection and classificationof heart murmurs and congenital heart disorders, such as ventricular andatrial septal defects. Furthermore, the PCG has been shown to be usefulfor cuffless blood pressure estimation when used in combination with thePPG to determine the vascular transit time, which is similar in conceptto the more commonly used pulse transit time.

Electrocardiography

Advanced Electrocardiography (Advanced-ECG) measures the voltageassociated with depolarization and repolarization during a cardiaccycle. The biopotentials, or currents in the body, are measured throughelectrodes, which are conductive pads attached to the skin. Disposablewet electrodes typically use Ag—AgCl contacts with electrolyte gel forconduction and dry electrodes use metal plating. A pair of electrodesmeasure the voltage potential difference between two points on the body.Heart rate and heart rate variability derivations can be determined byidentifying R peaks within QRS complexes in ECG signals and calculatingthe average and standard deviation as measured by beats per minute. ECGis a clinical reference for diagnosing cardiac arrhythmias (such asAtrial Fibrillation), Myocardial Infarction, and coronary arterydisease.

Advanced ECG methods leverage fundamental principles of biomechanics, asmodelled through the Moens-Korteweg equations, pulse wave velocity (PWV)can be mapped to characteristics of the neighboring arterial wall. Withan initial blood pressure calibration step, these equations can then bemanipulated to derive Systolic and Diastolic estimates from pulsetransit time as measured between ECG and photoplethysmography (PPG)peak-to-peak calculations.

Video Analysis

Recent advances have been made in the arena of non-contact, cuffless BPestimations from video recordings. PPG signals have been extracted fromfacial video recordings by enhancing the differential fromframe-to-frame on the green and/or red channels for pulse rate orrespiratory rate estimations. Blood pressure estimates are made from PTTby capturing video simultaneously of the face and hand. BP and HR havealso been estimated directly from PPG signals extracted from facialrecording through the analysis of time and frequency domaincharacteristics.

Radar Methods

Electromagnetic radar has demonstrated potential for non-invasive andremote sensing of vital sign measurements. A frequency modulatedcontinuous wave (FMCW) radar operating at 77 GHz can detect chest wallmovement, which is translated into heart rate and breathing rateestimates [1][2]. Radar technologies have also shown promise for remoteglucose monitoring [3].

Examples of this type of work includes the work of Alizadeh and Shakeret. al., who investigated use of frequency modulated continuous wave(FMCW) radar operating at 77 GHz to determine heart rates and breathingrates. See Alizadeh et. al, IEEE access VOLUME 7, 2019, pages 54958 to54968.

Trademarks: Corlanor® is a registered trademark of BIOFARMA société paractions simplifiée, Entresto® is a trademark of Novartis AG CORPORATIONSWITZERLAND, Contrave® is a trademark of Orexigen Therapeutics, Inc.Narcan is a trademark of ENDO LABORATORIES INC. and ADAPT PHARMAOPERATIONS LIMITED.

The invention claimed is:
 1. A method for determining a real-worldeffectiveness of at least one specific medication from a medicationregimen comprising a plurality of medications, said method comprising:obtaining patient operable instrumentation comprising a plurality ofphysiological monitoring devices configured to obtain a plurality ofdifferent types of actual patient pulse wave measurements comprisingelectrode-based time varying electrocardiograph (ECG) readings, and timevarying blood oxygen saturation levels, and time varying blood pressuremeasurements; said patient operable instrumentation further comprisingat least one processor and memory; wherein said patient operableinstrumentation and said physiological monitoring devices are aunitized, common processor controlled, system; obtaining a plurality ofindividual medication impact parameters, each individual medicationimpact parameter providing information on how an individual knownspecific medication alters a specific type of pulse wave measurements;obtaining a plurality of patient reference information, each individualpatient reference information providing information on a specific typeof patient baseline pulse wave measurement in an absence of patientmedication; wherein said plurality of medication impact parameters andplurality of patient reference information further provide informationon how they alter at least some of said plurality of different types ofactual patient pulse wave measurements; obtaining patient medicationschedule information regarding said plurality of medications andmedication dosing schedules for said patient, and storing said patientmedication schedule in said memory; obtaining and analyzing, using saidat least one processor, a plurality of different types of actual patientpulse wave measurements at a known time; calculating, using said atleast one processor, expected patient pulse wave measurements based onsaid patient medication schedule information and said known time andsaid plurality of patient reference information and said plurality ofmedication impact parameters; determining, using said at least oneprocessor, which of said plurality of different types of actual patientpulse wave measurements are inconsistent with said expected patientpulse wave measurements, thus producing specific medications withinconsistent findings, and using said processor to store at least saidspecific medications with inconsistent findings in said memory.
 2. Themethod of claim 1, further obtaining patient alarm information, saidpatient alarm information reporting on any of blood oxygen saturation,blood pressure measurements, or electrocardiograph readings that falloutside of previously established boundaries; and using said patientalarm information and those specific medications where inconsistentfindings were obtained to determine and report that said patient is atrisk of being ineffectively treated by those specific medications. 3.The method of claim 1, further using said processor to determine wheninconsistent findings were obtained over more than one different type ofactual patient pulse wave measurements; and using said processor todetermine that at least two different types of actual patient pulse wavemeasurements have inconsistent findings, and determining and reportingthat said patient is at risk of being ineffectively treated by one ormore specific medications.
 4. The method of claim 1, wherein saidplurality of medication impact parameters is determined by saidprocessor by from at least one of either: a) averages over a pluralityof other patients in a presence and absence of said specific medication,further computing difference parameters between said presence andabsence of said specific medication; b) data obtained from said patientin said presence and absence of said specific medication, furthercomputing difference parameters between said presence and absence ofsaid specific medication.
 5. The method of claim 1, wherein saidplurality of patient reference information is determined by saidprocessor from at least one of either: a) averages over a plurality ofother patients in an absence of said specific medication; b) dataobtained from said patient in said absence of said specific medication.6. The method of claim 1, wherein said expected patient pulse wavemeasurements are determined by said processor from said patientmedication schedule information and said known time and said pluralityof patient reference information and said plurality of medication impactparameters by using said processor to transform said plurality ofpatient reference information into said expected patient pulse wavemeasurements by the steps of: for each individual medication in saidpatient medication schedule information: a) using selected correspondingindividual medication impact parameters from said plurality ofmedication impact parameters to produce selected individual medicationimpact parameters; b) applying, either analytically or empirically, saidselected individual medication impact parameters to said plurality ofpatient reference information to produce intermediate transformedpatient reference information; c) producing said expected patient pulsewave measurements by repeating steps a and b until all individualmedication in said patient medication schedule information has beenprocessed.
 7. The method of claim 1, wherein said patient operableinstrumentation is further configured to receive compliance informationfrom said patient reporting on periods of at least one of patientmedication compliance or non-compliance, and to use said complianceinformation to select at least some actual patient pulse wavemeasurements for use in establishing or refining at least some of saidmedication impact parameters or said patient reference information. 8.The method of claim 1, further reporting at least some of thosemedications where inconsistent findings were determined by saidprocessor to a remote computerized device over a network; and storingleast some of those medication where inconsistent findings were obtainedin at least one remote network computer memory.
 9. The method of claim 1wherein any of said plurality of medication impact parameters, pluralityof patient reference information, and patient medication scheduleinformation is either uploaded or downloaded by said processor over anetwork from a remote computerized device.
 10. The method of claim 1,wherein said electrode-based time varying electrocardiograph (ECG)readings, and time varying blood oxygen saturation levels, and timevarying blood pressure measurements are all performed simultaneously.11. The method of claim 1, wherein said electrode-based time varyingelectrocardiograph (ECG) readings and said time varying blood oxygensaturation levels are simultaneously obtained using a same externalcombination finger pulse oximeter/ECG electrode device; and said timevarying blood pressure measurements are simultaneously obtained using ablood pressure monitoring device.
 12. The method of claim 1, whereinsaid electrode-based time varying electrocardiograph (ECG) readings, andtime varying blood oxygen saturation levels, and time varying bloodpressure measurements are all performed simultaneously so as to obtainsynchronized pulse wave, hemoglobin absorbance, and electrocardiogrampulse wave information of the same patient heart beats.
 13. The methodof claim 1, wherein said effectiveness comprises an impact of said atleast one specific medication on said patient's actual patient pulsewave measurements as compared to calculated expected patient pulse wavemeasurements for said medication regimen.
 14. A method for determining areal-world effectiveness of at least one specific medication from amedication regimen comprising a plurality of medications, said methodcomprising: obtaining patient operable instrumentation comprising aplurality of physiological monitoring devices configured to obtain aplurality of different types of actual patient pulse wave measurementscomprising electrode-based time varying electrocardiograph (ECG)readings, and time varying blood oxygen saturation levels, and timevarying blood pressure measurements; said patient operableinstrumentation further comprising at least one processor and memory;wherein said patient operable instrumentation and said physiologicalmonitoring devices are a unitized, common processor controlled, system;wherein said electrode-based time varying electrocardiograph (ECG)readings, and time varying blood oxygen saturation levels, and timevarying blood pressure measurements are all performed simultaneously soas to obtain synchronized pulse wave, hemoglobin absorbance, andelectrocardiogram pulse wave information of the same patient heartbeats; obtaining a plurality of individual medication impact parameters,each individual medication impact parameter providing information on howan individual known specific medication alters a specific type of pulsewave measurements; obtaining a plurality of patient referenceinformation, each individual patient reference information providinginformation on a specific type of patient baseline pulse wavemeasurement in an absence of patient medication; wherein said pluralityof medication impact parameters and plurality of patient referenceinformation further provide information on how they alter at least someof said plurality of different types of actual patient pulse wavemeasurements; obtaining patient medication schedule informationregarding said plurality of medications and medication dosing schedulesfor said patient, and storing said patient medication schedule in saidmemory; obtaining and analyzing, using said at least one processor, aplurality of different types of actual patient pulse wave measurementsat a known time; calculating, using said at least one processor,expected patient pulse wave measurements based on said patientmedication schedule information and said known time and said pluralityof patient reference information and said plurality of medication impactparameters; determining, using said at least one processor, which ofsaid plurality of different types of actual patient pulse wavemeasurements are inconsistent with said expected patient pulse wavemeasurements, thus producing specific medications with inconsistentfindings; using said processor to store at least said specificmedications with inconsistent findings in said memory; and further usingsaid processor to determine when inconsistent findings were obtainedover more than one different type of actual patient pulse wavemeasurements; and using said processor to determine that at least twodifferent types of actual patient pulse wave measurements haveinconsistent findings, and determining and reporting that said patientis at risk of being ineffectively treated by one or more specificmedications.
 15. A device for determining a real-world effectiveness ofat least one specific medication from a medication regimen comprising aplurality of medications, said device comprising: patient operableinstrumentation comprising physiological monitoring devices configuredto obtain a plurality of different types of actual patient pulse wavemeasurements comprising electrode-based time varying electrocardiograph(ECG) readings, and time varying blood oxygen saturation levels, andtime varying blood pressure measurements types; said patient operableinstrumentation further comprising at least one processor and memory;wherein said patient operable instrumentation and said physiologicalmonitoring devices are a unitized system, all managed by at least onecommon processor; wherein said device is configured to obtain and storea plurality of individual medication impact parameters, each individualmedication impact parameter providing information on how an individualknown specific medication alters a specific type of pulse wavemeasurements; wherein said plurality of medication impact parameters andplurality of patient reference information further provide informationon how they alter at least some of said plurality of different types ofactual patient pulse wave measurements; wherein said device is furtherconfigured to obtain and store a plurality of patient referenceinformation, each individual patient reference information providinginformation on a specific type of patient baseline pulse wavemeasurement in an absence of patient medication; wherein said device isfurther configured to obtain and store patient medication scheduleinformation regarding said at least one medication and medication dosingschedule for said patient; said at least one processor furtherconfigured so that when said patient operable instrumentation is used ona patient with patient medication schedule information, said at leastone processor is further configured to obtain a plurality of differenttypes of actual pulse wave measurement at a known time, said at leastone processor analyzes said plurality of different types of actualpatient pulse wave measurements at a known time, and determines which ofsaid plurality of different types of actual patient pulse wavemeasurements are inconsistent with those expected patient pulse wavemeasurements calculated from said patient medication scheduleinformation, said known time, said plurality of patient referenceinformation, and said plurality of medication impact parameters; whereinsaid at least one processor is further configured to store at leastthose specific medications where inconsistent findings were obtained insaid memory; wherein said effectiveness comprises an impact of said atleast one specific medication on a patient's actual patient pulse wavemeasurements as compared to calculated expected patient pulse wavemeasurements for said medication regimen.
 16. The device of claim 15,wherein said at least one processor is further configured to providepatient alarm information, said patient alarm information reporting onwhen any of blood oxygen saturation, blood pressure measurements, orelectrocardiograph readings fall outside of previously establishedboundaries; and wherein said device is configured to use said patientalarm information and those medications where inconsistent findings wereobtained to determine and report that said patient may be ineffectivelytreated by those specific medications.
 17. The device of claim 15,wherein said at least one processor is further configured to determineif inconsistent findings were obtained over more than one different typeof actual patient pulse wave measurements; and wherein said device isconfigured so that when at least two different types of actual patientpulse wave measurements have inconsistent findings, said device furtherdetermines and reports that said patient may be ineffectively treated bythose specific medications.
 18. The device of claim 15, wherein said atleast one processor is further configured to calculate said expectedpatient pulse wave measurements from said patient medication scheduleinformation, said known time, said plurality of patient referenceinformation, and said plurality of medication impact parameters by usingsaid processor to transform said plurality of patient referenceinformation into said expected patient pulse wave measurements by thesteps of: for each individual medication in said patient medicationschedule information: a) selecting corresponding individual medicationimpact parameters from said plurality of medication impact parameters,thus producing selected individual medication impact parameters; b)applying, either analytically or empirically, said selected individualmedication impact parameters to said plurality of patient referenceinformation, thus producing intermediate transformed patient referenceinformation; c) repeating steps a and b until all individual medicationin said patient medication schedule information has been processed, thusproducing said expected patient pulse wave measurements.
 19. The deviceof claim 15, wherein said patient operable instrumentation is furthercomprises means to receive compliance information from said patientreporting on periods of at least one of patient medication compliance ornon-compliance; and wherein said at least one processor is furtherconfigured to use said compliance information to select at least someactual patient pulse wave measurements for use in establishing orrefining at least some of said medication impact parameters or saidpatient reference information.
 20. The device of claim 15, furtherconfigured with network interface means to allow said device to connectwith at least one remote computerized device over a network; whereinsaid at least one processor is further configured report at least someof those medications where inconsistent findings were obtained to saidremote computerized device; and/or wherein said at least one processoris further configured to enable any of said plurality of medicationimpact parameters, plurality of patient reference information, andpatient medication schedule information to be either uploaded ordownloaded over a network from a remote computerized device.
 21. Thedevice of claim 15, further configured with at least one of a bodytemperature sensor and an accelerometer sensor, at least one lungfunction sensor, and a time varying electroencephalographic (EEG)reading sensor; wherein said plurality of patient reference informationfurther comprises at least one of baseline body temperature information,baseline patient motion data, baseline patient lung status data, orbaseline patient EEG data; wherein at least some of said medicationimpact parameters further provide information on how a specificmedication alters at least one of said baseline body temperatureinformation, said baseline patient motion data, baseline patient lungfunction, and said patient baseline EEG data; wherein said at least oneprocessor is further configured to use said patient medication scheduleinformation, said known time, said plurality of patient referenceinformation and at least some of said medication impact parameters tofurther determine if at least some specific medications are producinginconsistent findings.
 22. The device of claim 15, wherein said deviceis configured to perform said electrode-based time varyingelectrocardiograph (ECG) readings, and time varying blood oxygensaturation levels, and time varying blood pressure measurementssimultaneously so as to obtain synchronized pulse wave, hemoglobinabsorbance, and electrocardiogram pulse wave information of the samepatient heart beats.